Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations7770
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory971.2 KiB
Average record size in memory128.0 B

Variable types

Text8
Categorical2
DateTime1
Numeric4
Unsupported1

Alerts

age_on_netflix is highly overall correlated with release_yearHigh correlation
release_year is highly overall correlated with age_on_netflixHigh correlation
show_id has unique values Unique
title has unique values Unique
bigrams is an unsupported type, check if it needs cleaning or further analysis Unsupported
age_on_netflix has 2823 (36.3%) zeros Zeros

Reproduction

Analysis started2025-09-10 13:41:19.340617
Analysis finished2025-09-10 13:41:41.864771
Duration22.52 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

show_id
Text

Unique 

Distinct7770
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:42.492193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8580438
Min length2

Characters and Unicode

Total characters37747
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7770 ?
Unique (%)100.0%

Sample

1st rows1
2nd rows2
3rd rows3
4th rows4
5th rows5
ValueCountFrequency (%)
s11 1
 
< 0.1%
s7787 1
 
< 0.1%
s1 1
 
< 0.1%
s2 1
 
< 0.1%
s3 1
 
< 0.1%
s4 1
 
< 0.1%
s5 1
 
< 0.1%
s6 1
 
< 0.1%
s7 1
 
< 0.1%
s8 1
 
< 0.1%
Other values (7760) 7760
99.9%
2025-09-10T13:41:43.153955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 7770
20.6%
1 3357
8.9%
4 3354
8.9%
5 3352
8.9%
2 3351
8.9%
6 3350
8.9%
3 3344
8.9%
7 3129
8.3%
8 2252
 
6.0%
0 2245
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 7770
20.6%
1 3357
8.9%
4 3354
8.9%
5 3352
8.9%
2 3351
8.9%
6 3350
8.9%
3 3344
8.9%
7 3129
8.3%
8 2252
 
6.0%
0 2245
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 7770
20.6%
1 3357
8.9%
4 3354
8.9%
5 3352
8.9%
2 3351
8.9%
6 3350
8.9%
3 3344
8.9%
7 3129
8.3%
8 2252
 
6.0%
0 2245
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 7770
20.6%
1 3357
8.9%
4 3354
8.9%
5 3352
8.9%
2 3351
8.9%
6 3350
8.9%
3 3344
8.9%
7 3129
8.3%
8 2252
 
6.0%
0 2245
 
5.9%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
Movie
5372 
TV Show
2398 

Length

Max length7
Median length5
Mean length5.6172458
Min length5

Characters and Unicode

Total characters43646
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV Show
2nd rowMovie
3rd rowMovie
4th rowMovie
5th rowMovie

Common Values

ValueCountFrequency (%)
Movie 5372
69.1%
TV Show 2398
30.9%

Length

2025-09-10T13:41:43.291597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-10T13:41:43.371042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
movie 5372
52.8%
tv 2398
23.6%
show 2398
23.6%

Most occurring characters

ValueCountFrequency (%)
o 7770
17.8%
M 5372
12.3%
v 5372
12.3%
i 5372
12.3%
e 5372
12.3%
T 2398
 
5.5%
V 2398
 
5.5%
2398
 
5.5%
S 2398
 
5.5%
h 2398
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43646
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7770
17.8%
M 5372
12.3%
v 5372
12.3%
i 5372
12.3%
e 5372
12.3%
T 2398
 
5.5%
V 2398
 
5.5%
2398
 
5.5%
S 2398
 
5.5%
h 2398
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43646
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7770
17.8%
M 5372
12.3%
v 5372
12.3%
i 5372
12.3%
e 5372
12.3%
T 2398
 
5.5%
V 2398
 
5.5%
2398
 
5.5%
S 2398
 
5.5%
h 2398
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43646
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7770
17.8%
M 5372
12.3%
v 5372
12.3%
i 5372
12.3%
e 5372
12.3%
T 2398
 
5.5%
V 2398
 
5.5%
2398
 
5.5%
S 2398
 
5.5%
h 2398
 
5.5%

title
Text

Unique 

Distinct7770
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:43.714267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length104
Median length71
Mean length17.622008
Min length1

Characters and Unicode

Total characters136923
Distinct characters197
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7770 ?
Unique (%)100.0%

Sample

1st row3%
2nd row7:19
3rd row23:59
4th row9
5th row21
ValueCountFrequency (%)
the 1928
 
8.0%
of 608
 
2.5%
a 306
 
1.3%
in 244
 
1.0%
216
 
0.9%
and 200
 
0.8%
to 171
 
0.7%
love 151
 
0.6%
my 126
 
0.5%
2 113
 
0.5%
Other values (8480) 19906
83.0%
2025-09-10T13:41:44.258434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16201
 
11.8%
e 12729
 
9.3%
a 9811
 
7.2%
o 7710
 
5.6%
i 7568
 
5.5%
r 7344
 
5.4%
n 7147
 
5.2%
t 6211
 
4.5%
s 5461
 
4.0%
h 4754
 
3.5%
Other values (187) 51987
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16201
 
11.8%
e 12729
 
9.3%
a 9811
 
7.2%
o 7710
 
5.6%
i 7568
 
5.5%
r 7344
 
5.4%
n 7147
 
5.2%
t 6211
 
4.5%
s 5461
 
4.0%
h 4754
 
3.5%
Other values (187) 51987
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16201
 
11.8%
e 12729
 
9.3%
a 9811
 
7.2%
o 7710
 
5.6%
i 7568
 
5.5%
r 7344
 
5.4%
n 7147
 
5.2%
t 6211
 
4.5%
s 5461
 
4.0%
h 4754
 
3.5%
Other values (187) 51987
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16201
 
11.8%
e 12729
 
9.3%
a 9811
 
7.2%
o 7710
 
5.6%
i 7568
 
5.5%
r 7344
 
5.4%
n 7147
 
5.2%
t 6211
 
4.5%
s 5461
 
4.0%
h 4754
 
3.5%
Other values (187) 51987
38.0%
Distinct4048
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:44.615585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length208
Median length179
Mean length12.823295
Min length2

Characters and Unicode

Total characters99637
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3311 ?
Unique (%)42.6%

Sample

1st rowUnknown
2nd rowJorge Michel Grau
3rd rowGilbert Chan
4th rowShane Acker
5th rowRobert Luketic
ValueCountFrequency (%)
unknown 2376
 
15.6%
david 108
 
0.7%
michael 103
 
0.7%
john 82
 
0.5%
paul 64
 
0.4%
robert 48
 
0.3%
chris 46
 
0.3%
peter 44
 
0.3%
james 42
 
0.3%
jay 41
 
0.3%
Other values (5898) 12292
80.6%
2025-09-10T13:41:45.121894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 12593
 
12.6%
a 8846
 
8.9%
7476
 
7.5%
o 6684
 
6.7%
e 6145
 
6.2%
i 5129
 
5.1%
r 4940
 
5.0%
k 3615
 
3.6%
l 3382
 
3.4%
h 2908
 
2.9%
Other values (89) 37919
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 12593
 
12.6%
a 8846
 
8.9%
7476
 
7.5%
o 6684
 
6.7%
e 6145
 
6.2%
i 5129
 
5.1%
r 4940
 
5.0%
k 3615
 
3.6%
l 3382
 
3.4%
h 2908
 
2.9%
Other values (89) 37919
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 12593
 
12.6%
a 8846
 
8.9%
7476
 
7.5%
o 6684
 
6.7%
e 6145
 
6.2%
i 5129
 
5.1%
r 4940
 
5.0%
k 3615
 
3.6%
l 3382
 
3.4%
h 2908
 
2.9%
Other values (89) 37919
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 12593
 
12.6%
a 8846
 
8.9%
7476
 
7.5%
o 6684
 
6.7%
e 6145
 
6.2%
i 5129
 
5.1%
r 4940
 
5.0%
k 3615
 
3.6%
l 3382
 
3.4%
h 2908
 
2.9%
Other values (89) 37919
38.1%

cast
Text

Distinct6818
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:45.516105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length771
Median length358
Mean length107.66396
Min length3

Characters and Unicode

Total characters836549
Distinct characters150
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6680 ?
Unique (%)86.0%

Sample

1st rowJoão Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Mel Fronckowiak, Sergio Mamberti, Zezé Motta, Celso Frateschi
2nd rowDemián Bichir, Héctor Bonilla, Oscar Serrano, Azalia Ortiz, Octavio Michel, Carmen Beato
3rd rowTedd Chan, Stella Chung, Henley Hii, Lawrence Koh, Tommy Kuan, Josh Lai, Mark Lee, Susan Leong, Benjamin Lim
4th rowElijah Wood, John C. Reilly, Jennifer Connelly, Christopher Plummer, Crispin Glover, Martin Landau, Fred Tatasciore, Alan Oppenheimer, Tom Kane
5th rowJim Sturgess, Kevin Spacey, Kate Bosworth, Aaron Yoo, Liza Lapira, Jacob Pitts, Laurence Fishburne, Jack McGee, Josh Gad, Sam Golzari, Helen Carey, Jack Gilpin
ValueCountFrequency (%)
unknown 718
 
0.6%
michael 567
 
0.5%
john 500
 
0.4%
david 484
 
0.4%
lee 395
 
0.3%
james 357
 
0.3%
paul 310
 
0.3%
kim 298
 
0.3%
khan 255
 
0.2%
de 253
 
0.2%
Other values (30345) 111562
96.4%
2025-09-10T13:41:46.089826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107934
 
12.9%
a 82879
 
9.9%
e 57109
 
6.8%
n 52504
 
6.3%
i 48927
 
5.8%
, 48793
 
5.8%
r 42344
 
5.1%
o 39266
 
4.7%
l 30889
 
3.7%
h 25092
 
3.0%
Other values (140) 300812
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 836549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
107934
 
12.9%
a 82879
 
9.9%
e 57109
 
6.8%
n 52504
 
6.3%
i 48927
 
5.8%
, 48793
 
5.8%
r 42344
 
5.1%
o 39266
 
4.7%
l 30889
 
3.7%
h 25092
 
3.0%
Other values (140) 300812
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 836549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
107934
 
12.9%
a 82879
 
9.9%
e 57109
 
6.8%
n 52504
 
6.3%
i 48927
 
5.8%
, 48793
 
5.8%
r 42344
 
5.1%
o 39266
 
4.7%
l 30889
 
3.7%
h 25092
 
3.0%
Other values (140) 300812
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 836549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
107934
 
12.9%
a 82879
 
9.9%
e 57109
 
6.8%
n 52504
 
6.3%
i 48927
 
5.8%
, 48793
 
5.8%
r 42344
 
5.1%
o 39266
 
4.7%
l 30889
 
3.7%
h 25092
 
3.0%
Other values (140) 300812
36.0%
Distinct681
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:46.349226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length123
Median length104
Mean length12.444916
Min length4

Characters and Unicode

Total characters96697
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique517 ?
Unique (%)6.7%

Sample

1st rowBrazil
2nd rowMexico
3rd rowSingapore
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united 4549
31.2%
states 3793
26.0%
india 990
 
6.8%
kingdom 722
 
5.0%
canada 412
 
2.8%
france 349
 
2.4%
japan 285
 
2.0%
south 266
 
1.8%
spain 215
 
1.5%
korea 212
 
1.5%
Other values (119) 2784
19.1%
2025-09-10T13:41:46.791073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 13008
13.5%
e 10418
10.8%
a 9707
10.0%
n 9118
9.4%
i 8241
8.5%
d 7075
 
7.3%
6807
 
7.0%
U 4570
 
4.7%
S 4401
 
4.6%
s 4334
 
4.5%
Other values (41) 19018
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 13008
13.5%
e 10418
10.8%
a 9707
10.0%
n 9118
9.4%
i 8241
8.5%
d 7075
 
7.3%
6807
 
7.0%
U 4570
 
4.7%
S 4401
 
4.6%
s 4334
 
4.5%
Other values (41) 19018
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 13008
13.5%
e 10418
10.8%
a 9707
10.0%
n 9118
9.4%
i 8241
8.5%
d 7075
 
7.3%
6807
 
7.0%
U 4570
 
4.7%
S 4401
 
4.6%
s 4334
 
4.5%
Other values (41) 19018
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 13008
13.5%
e 10418
10.8%
a 9707
10.0%
n 9118
9.4%
i 8241
8.5%
d 7075
 
7.3%
6807
 
7.0%
U 4570
 
4.7%
S 4401
 
4.6%
s 4334
 
4.5%
Other values (41) 19018
19.7%
Distinct1511
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
Minimum2008-01-01 00:00:00
Maximum2021-01-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-10T13:41:47.005999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:47.228984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

release_year
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.9354
Minimum1925
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:47.442757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1925
5-th percentile1998
Q12013
median2017
Q32018
95-th percentile2020
Maximum2021
Range96
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.7643567
Coefficient of variation (CV)0.0043518559
Kurtosis17.54936
Mean2013.9354
Median Absolute Deviation (MAD)2
Skewness-3.6188757
Sum15648278
Variance76.813949
MonotonicityNot monotonic
2025-09-10T13:41:47.674202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018 1120
14.4%
2017 1010
13.0%
2019 996
12.8%
2016 881
11.3%
2020 868
11.2%
2015 536
 
6.9%
2014 334
 
4.3%
2013 265
 
3.4%
2012 218
 
2.8%
2010 171
 
2.2%
Other values (63) 1371
17.6%
ValueCountFrequency (%)
1925 1
 
< 0.1%
1942 2
< 0.1%
1943 3
< 0.1%
1944 3
< 0.1%
1945 3
< 0.1%
1946 2
< 0.1%
1947 1
 
< 0.1%
1954 2
< 0.1%
1955 3
< 0.1%
1956 2
< 0.1%
ValueCountFrequency (%)
2021 31
 
0.4%
2020 868
11.2%
2019 996
12.8%
2018 1120
14.4%
2017 1010
13.0%
2016 881
11.3%
2015 536
6.9%
2014 334
 
4.3%
2013 265
 
3.4%
2012 218
 
2.8%

rating
Categorical

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
TV-MA
2861 
TV-14
1928 
TV-PG
804 
R
665 
PG-13
386 
Other values (9)
1126 

Length

Max length8
Median length5
Mean length4.4496782
Min length1

Characters and Unicode

Total characters34574
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV-MA
2nd rowTV-MA
3rd rowR
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
TV-MA 2861
36.8%
TV-14 1928
24.8%
TV-PG 804
 
10.3%
R 665
 
8.6%
PG-13 386
 
5.0%
TV-Y 279
 
3.6%
TV-Y7 270
 
3.5%
PG 247
 
3.2%
TV-G 194
 
2.5%
NR 83
 
1.1%
Other values (4) 53
 
0.7%

Length

2025-09-10T13:41:47.828968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tv-ma 2861
36.8%
tv-14 1928
24.8%
tv-pg 804
 
10.3%
r 665
 
8.6%
pg-13 386
 
5.0%
tv-y 279
 
3.6%
tv-y7 270
 
3.5%
pg 247
 
3.2%
tv-g 194
 
2.5%
nr 83
 
1.1%
Other values (4) 53
 
0.7%

Most occurring characters

ValueCountFrequency (%)
- 6737
19.5%
V 6348
18.4%
T 6342
18.3%
M 2861
8.3%
A 2861
8.3%
1 2317
 
6.7%
4 1928
 
5.6%
G 1670
 
4.8%
P 1437
 
4.2%
R 753
 
2.2%
Other values (7) 1320
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 6737
19.5%
V 6348
18.4%
T 6342
18.3%
M 2861
8.3%
A 2861
8.3%
1 2317
 
6.7%
4 1928
 
5.6%
G 1670
 
4.8%
P 1437
 
4.2%
R 753
 
2.2%
Other values (7) 1320
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 6737
19.5%
V 6348
18.4%
T 6342
18.3%
M 2861
8.3%
A 2861
8.3%
1 2317
 
6.7%
4 1928
 
5.6%
G 1670
 
4.8%
P 1437
 
4.2%
R 753
 
2.2%
Other values (7) 1320
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 6737
19.5%
V 6348
18.4%
T 6342
18.3%
M 2861
8.3%
A 2861
8.3%
1 2317
 
6.7%
4 1928
 
5.6%
G 1670
 
4.8%
P 1437
 
4.2%
R 753
 
2.2%
Other values (7) 1320
 
3.8%
Distinct216
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:48.215851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.0459459
Min length5

Characters and Unicode

Total characters54747
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.5%

Sample

1st row4 Seasons
2nd row93 min
3rd row78 min
4th row80 min
5th row123 min
ValueCountFrequency (%)
min 5372
34.6%
1 1606
 
10.3%
season 1606
 
10.3%
seasons 792
 
5.1%
2 378
 
2.4%
3 184
 
1.2%
90 136
 
0.9%
93 131
 
0.8%
94 125
 
0.8%
91 125
 
0.8%
Other values (199) 5085
32.7%
2025-09-10T13:41:48.688253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7770
14.2%
n 7770
14.2%
m 5372
9.8%
i 5372
9.8%
1 5275
9.6%
s 3190
 
5.8%
S 2398
 
4.4%
o 2398
 
4.4%
e 2398
 
4.4%
a 2398
 
4.4%
Other values (9) 10406
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7770
14.2%
n 7770
14.2%
m 5372
9.8%
i 5372
9.8%
1 5275
9.6%
s 3190
 
5.8%
S 2398
 
4.4%
o 2398
 
4.4%
e 2398
 
4.4%
a 2398
 
4.4%
Other values (9) 10406
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7770
14.2%
n 7770
14.2%
m 5372
9.8%
i 5372
9.8%
1 5275
9.6%
s 3190
 
5.8%
S 2398
 
4.4%
o 2398
 
4.4%
e 2398
 
4.4%
a 2398
 
4.4%
Other values (9) 10406
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7770
14.2%
n 7770
14.2%
m 5372
9.8%
i 5372
9.8%
1 5275
9.6%
s 3190
 
5.8%
S 2398
 
4.4%
o 2398
 
4.4%
e 2398
 
4.4%
a 2398
 
4.4%
Other values (9) 10406
19.0%
Distinct491
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:48.899768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length58
Mean length33.372458
Min length6

Characters and Unicode

Total characters259304
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)1.8%

Sample

1st rowInternational TV Shows, TV Dramas, TV Sci-Fi & Fantasy
2nd rowDramas, International Movies
3rd rowHorror Movies, International Movies
4th rowAction & Adventure, Independent Movies, Sci-Fi & Fantasy
5th rowDramas
ValueCountFrequency (%)
movies 4985
14.4%
tv 4951
14.3%
international 3634
10.5%
dramas 2808
 
8.1%
shows 2607
 
7.5%
2235
 
6.5%
comedies 1988
 
5.7%
action 870
 
2.5%
adventure 870
 
2.5%
romantic 864
 
2.5%
Other values (33) 8763
25.3%
2025-09-10T13:41:49.299196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26805
 
10.3%
e 22192
 
8.6%
i 18859
 
7.3%
n 18307
 
7.1%
a 17600
 
6.8%
o 17575
 
6.8%
s 17298
 
6.7%
t 13141
 
5.1%
r 12645
 
4.9%
, 9272
 
3.6%
Other values (33) 85610
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 259304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26805
 
10.3%
e 22192
 
8.6%
i 18859
 
7.3%
n 18307
 
7.1%
a 17600
 
6.8%
o 17575
 
6.8%
s 17298
 
6.7%
t 13141
 
5.1%
r 12645
 
4.9%
, 9272
 
3.6%
Other values (33) 85610
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 259304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26805
 
10.3%
e 22192
 
8.6%
i 18859
 
7.3%
n 18307
 
7.1%
a 17600
 
6.8%
o 17575
 
6.8%
s 17298
 
6.7%
t 13141
 
5.1%
r 12645
 
4.9%
, 9272
 
3.6%
Other values (33) 85610
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 259304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26805
 
10.3%
e 22192
 
8.6%
i 18859
 
7.3%
n 18307
 
7.1%
a 17600
 
6.8%
o 17575
 
6.8%
s 17298
 
6.7%
t 13141
 
5.1%
r 12645
 
4.9%
, 9272
 
3.6%
Other values (33) 85610
33.0%
Distinct7752
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size121.4 KiB
2025-09-10T13:41:49.662679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length248
Median length240
Mean length143.10129
Min length61

Characters and Unicode

Total characters1111897
Distinct characters117
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7736 ?
Unique (%)99.6%

Sample

1st rowIn a future where the elite inhabit an island paradise far from the crowded slums, you get one chance to join the 3% saved from squalor.
2nd rowAfter a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued while trying desperately to stay alive.
3rd rowWhen an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunting their jungle island training camp.
4th rowIn a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until a brave newcomer joins the group.
5th rowA brilliant group of students become card-counting experts with the intent of swindling millions out of Las Vegas casinos by playing blackjack.
ValueCountFrequency (%)
a 10128
 
5.5%
the 7181
 
3.9%
to 5652
 
3.1%
and 5585
 
3.0%
of 4692
 
2.5%
in 3857
 
2.1%
his 3003
 
1.6%
with 1972
 
1.1%
her 1882
 
1.0%
an 1727
 
0.9%
Other values (20204) 139470
75.3%
2025-09-10T13:41:50.203384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
177385
16.0%
e 104477
 
9.4%
a 74649
 
6.7%
t 71698
 
6.4%
i 69304
 
6.2%
n 65693
 
5.9%
o 64011
 
5.8%
s 63958
 
5.8%
r 62394
 
5.6%
h 42862
 
3.9%
Other values (107) 315466
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1111897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
177385
16.0%
e 104477
 
9.4%
a 74649
 
6.7%
t 71698
 
6.4%
i 69304
 
6.2%
n 65693
 
5.9%
o 64011
 
5.8%
s 63958
 
5.8%
r 62394
 
5.6%
h 42862
 
3.9%
Other values (107) 315466
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1111897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
177385
16.0%
e 104477
 
9.4%
a 74649
 
6.7%
t 71698
 
6.4%
i 69304
 
6.2%
n 65693
 
5.9%
o 64011
 
5.8%
s 63958
 
5.8%
r 62394
 
5.6%
h 42862
 
3.9%
Other values (107) 315466
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1111897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
177385
16.0%
e 104477
 
9.4%
a 74649
 
6.7%
t 71698
 
6.4%
i 69304
 
6.2%
n 65693
 
5.9%
o 64011
 
5.8%
s 63958
 
5.8%
r 62394
 
5.6%
h 42862
 
3.9%
Other values (107) 315466
28.4%

year_added
Real number (ℝ)

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.495
Minimum2008
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.1 KiB
2025-09-10T13:41:50.309573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2016
Q12018
median2019
Q32020
95-th percentile2020
Maximum2021
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3875819
Coefficient of variation (CV)0.0006874339
Kurtosis2.6893542
Mean2018.495
Median Absolute Deviation (MAD)1
Skewness-1.0091554
Sum15683706
Variance1.9253835
MonotonicityNot monotonic
2025-09-10T13:41:50.410235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2019 2153
27.7%
2020 2009
25.9%
2018 1684
21.7%
2017 1222
15.7%
2016 440
 
5.7%
2021 117
 
1.5%
2015 88
 
1.1%
2014 25
 
0.3%
2011 13
 
0.2%
2013 11
 
0.1%
Other values (4) 8
 
0.1%
ValueCountFrequency (%)
2008 2
 
< 0.1%
2009 2
 
< 0.1%
2010 1
 
< 0.1%
2011 13
 
0.2%
2012 3
 
< 0.1%
2013 11
 
0.1%
2014 25
 
0.3%
2015 88
 
1.1%
2016 440
 
5.7%
2017 1222
15.7%
ValueCountFrequency (%)
2021 117
 
1.5%
2020 2009
25.9%
2019 2153
27.7%
2018 1684
21.7%
2017 1222
15.7%
2016 440
 
5.7%
2015 88
 
1.1%
2014 25
 
0.3%
2013 11
 
0.1%
2012 3
 
< 0.1%

month_added
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7849421
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.1 KiB
2025-09-10T13:41:50.507478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5912186
Coefficient of variation (CV)0.52929245
Kurtosis-1.2811157
Mean6.7849421
Median Absolute Deviation (MAD)3
Skewness-0.12036778
Sum52719
Variance12.896851
MonotonicityNot monotonic
2025-09-10T13:41:50.595790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 832
10.7%
10 785
10.1%
1 756
9.7%
11 738
9.5%
3 668
8.6%
9 618
8.0%
8 617
7.9%
7 600
7.7%
4 600
7.7%
5 543
7.0%
Other values (2) 1013
13.0%
ValueCountFrequency (%)
1 756
9.7%
2 471
6.1%
3 668
8.6%
4 600
7.7%
5 543
7.0%
6 542
7.0%
7 600
7.7%
8 617
7.9%
9 618
8.0%
10 785
10.1%
ValueCountFrequency (%)
12 832
10.7%
11 738
9.5%
10 785
10.1%
9 618
8.0%
8 617
7.9%
7 600
7.7%
6 542
7.0%
5 543
7.0%
4 600
7.7%
3 668
8.6%

age_on_netflix
Real number (ℝ)

High correlation  Zeros 

Distinct74
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5595882
Minimum-3
Maximum93
Zeros2823
Zeros (%)36.3%
Negative12
Negative (%)0.2%
Memory size121.4 KiB
2025-09-10T13:41:51.097633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile0
Q10
median1
Q35
95-th percentile21
Maximum93
Range96
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.734355
Coefficient of variation (CV)1.9156017
Kurtosis17.585079
Mean4.5595882
Median Absolute Deviation (MAD)1
Skewness3.6754387
Sum35428
Variance76.288957
MonotonicityNot monotonic
2025-09-10T13:41:51.255683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2823
36.3%
1 1484
19.1%
2 643
 
8.3%
3 437
 
5.6%
4 336
 
4.3%
5 226
 
2.9%
6 217
 
2.8%
7 169
 
2.2%
8 161
 
2.1%
9 139
 
1.8%
Other values (64) 1135
14.6%
ValueCountFrequency (%)
-3 1
 
< 0.1%
-2 1
 
< 0.1%
-1 10
 
0.1%
0 2823
36.3%
1 1484
19.1%
2 643
 
8.3%
3 437
 
5.6%
4 336
 
4.3%
5 226
 
2.9%
6 217
 
2.8%
ValueCountFrequency (%)
93 1
 
< 0.1%
75 2
< 0.1%
74 3
< 0.1%
73 3
< 0.1%
72 3
< 0.1%
71 2
< 0.1%
70 1
 
< 0.1%
66 2
< 0.1%
65 2
< 0.1%
64 2
< 0.1%

bigrams
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size121.4 KiB

Interactions

2025-09-10T13:41:40.161684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:38.497921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.112027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.611999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:40.335093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:38.715435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.228153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.729005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:40.993387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:38.890822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.353879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.840992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:41.146102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.007905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.477131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-10T13:41:39.997139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-10T13:41:51.353502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
age_on_netflixmonth_addedratingrelease_yeartypeyear_added
age_on_netflix1.000-0.1150.135-0.8610.1750.044
month_added-0.1151.0000.0450.0150.020-0.123
rating0.1350.0451.0000.1280.3440.098
release_year-0.8610.0150.1281.0000.1590.365
type0.1750.0200.3440.1591.0000.067
year_added0.044-0.1230.0980.3650.0671.000

Missing values

2025-09-10T13:41:41.405799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-10T13:41:41.662216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionyear_addedmonth_addedage_on_netflixbigrams
0s1TV Show3%UnknownJoão Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Mel Fronckowiak, Sergio Mamberti, Zezé Motta, Celso FrateschiBrazil2020-08-142020TV-MA4 SeasonsInternational TV Shows, TV Dramas, TV Sci-Fi & FantasyIn a future where the elite inhabit an island paradise far from the crowded slums, you get one chance to join the 3% saved from squalor.202080[(in, a), (a, future), (future, where), (where, the), (the, elite), (elite, inhabit), (inhabit, an), (an, island), (island, paradise), (paradise, far), (far, from), (from, the), (the, crowded), (crowded, slums,), (slums,, you), (you, get), (get, one), (one, chance), (chance, to), (to, join), (join, the), (the, 3%), (3%, saved), (saved, from), (from, squalor.)]
1s2Movie7:19Jorge Michel GrauDemián Bichir, Héctor Bonilla, Oscar Serrano, Azalia Ortiz, Octavio Michel, Carmen BeatoMexico2016-12-232016TV-MA93 minDramas, International MoviesAfter a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued while trying desperately to stay alive.2016120[(after, a), (a, devastating), (devastating, earthquake), (earthquake, hits), (hits, mexico), (mexico, city,), (city,, trapped), (trapped, survivors), (survivors, from), (from, all), (all, walks), (walks, of), (of, life), (life, wait), (wait, to), (to, be), (be, rescued), (rescued, while), (while, trying), (trying, desperately), (desperately, to), (to, stay), (stay, alive.)]
2s3Movie23:59Gilbert ChanTedd Chan, Stella Chung, Henley Hii, Lawrence Koh, Tommy Kuan, Josh Lai, Mark Lee, Susan Leong, Benjamin LimSingapore2018-12-202011R78 minHorror Movies, International MoviesWhen an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunting their jungle island training camp.2018127[(when, an), (an, army), (army, recruit), (recruit, is), (is, found), (found, dead,), (dead,, his), (his, fellow), (fellow, soldiers), (soldiers, are), (are, forced), (forced, to), (to, confront), (confront, a), (a, terrifying), (terrifying, secret), (secret, that's), (that's, haunting), (haunting, their), (their, jungle), (jungle, island), (island, training), (training, camp.)]
3s4Movie9Shane AckerElijah Wood, John C. Reilly, Jennifer Connelly, Christopher Plummer, Crispin Glover, Martin Landau, Fred Tatasciore, Alan Oppenheimer, Tom KaneUnited States2017-11-162009PG-1380 minAction & Adventure, Independent Movies, Sci-Fi & FantasyIn a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until a brave newcomer joins the group.2017118[(in, a), (a, postapocalyptic), (postapocalyptic, world,), (world,, rag-doll), (rag-doll, robots), (robots, hide), (hide, in), (in, fear), (fear, from), (from, dangerous), (dangerous, machines), (machines, out), (out, to), (to, exterminate), (exterminate, them,), (them,, until), (until, a), (a, brave), (brave, newcomer), (newcomer, joins), (joins, the), (the, group.)]
4s5Movie21Robert LuketicJim Sturgess, Kevin Spacey, Kate Bosworth, Aaron Yoo, Liza Lapira, Jacob Pitts, Laurence Fishburne, Jack McGee, Josh Gad, Sam Golzari, Helen Carey, Jack GilpinUnited States2020-01-012008PG-13123 minDramasA brilliant group of students become card-counting experts with the intent of swindling millions out of Las Vegas casinos by playing blackjack.2020112[(a, brilliant), (brilliant, group), (group, of), (of, students), (students, become), (become, card-counting), (card-counting, experts), (experts, with), (with, the), (the, intent), (intent, of), (of, swindling), (swindling, millions), (millions, out), (out, of), (of, las), (las, vegas), (vegas, casinos), (casinos, by), (by, playing), (playing, blackjack.)]
5s6TV Show46Serdar AkarErdal Beşikçioğlu, Yasemin Allen, Melis Birkan, Saygın Soysal, Berkan Şal, Metin Belgin, Ayça Eren, Selin Uludoğan, Özay Fecht, Suna YıldızoğluTurkey2017-07-012016TV-MA1 SeasonInternational TV Shows, TV Dramas, TV MysteriesA genetics professor experiments with a treatment for his comatose sister that blends medical and shamanic cures, but unlocks a shocking side effect.201771[(a, genetics), (genetics, professor), (professor, experiments), (experiments, with), (with, a), (a, treatment), (treatment, for), (for, his), (his, comatose), (comatose, sister), (sister, that), (that, blends), (blends, medical), (medical, and), (and, shamanic), (shamanic, cures,), (cures,, but), (but, unlocks), (unlocks, a), (a, shocking), (shocking, side), (side, effect.)]
6s7Movie122Yasir Al YasiriAmina Khalil, Ahmed Dawood, Tarek Lotfy, Ahmed El Fishawy, Mahmoud Hijazi, Jihane Khalil, Asmaa Galal, Tara EmadEgypt2020-06-012019TV-MA95 minHorror Movies, International MoviesAfter an awful accident, a couple admitted to a grisly hospital are separated and must find each other to escape — before death finds them.202061[(after, an), (an, awful), (awful, accident,), (accident,, a), (a, couple), (couple, admitted), (admitted, to), (to, a), (a, grisly), (grisly, hospital), (hospital, are), (are, separated), (separated, and), (and, must), (must, find), (find, each), (each, other), (other, to), (to, escape), (escape, —), (—, before), (before, death), (death, finds), (finds, them.)]
7s8Movie187Kevin ReynoldsSamuel L. Jackson, John Heard, Kelly Rowan, Clifton Collins Jr., Tony PlanaUnited States2019-11-011997R119 minDramasAfter one of his high school students attacks him, dedicated teacher Trevor Garfield grows weary of the gang warfare in the New York City school system and moves to California to teach there, thinking it must be a less hostile environment.20191122[(after, one), (one, of), (of, his), (his, high), (high, school), (school, students), (students, attacks), (attacks, him,), (him,, dedicated), (dedicated, teacher), (teacher, trevor), (trevor, garfield), (garfield, grows), (grows, weary), (weary, of), (of, the), (the, gang), (gang, warfare), (warfare, in), (in, the), (the, new), (new, york), (york, city), (city, school), (school, system), (system, and), (and, moves), (moves, to), (to, california), (california, to), (to, teach), (teach, there,), (there,, thinking), (thinking, it), (it, must), (must, be), (be, a), (a, less), (less, hostile), (hostile, environment.)]
8s9Movie706Shravan KumarDivya Dutta, Atul Kulkarni, Mohan Agashe, Anupam Shyam, Raayo S. Bakhirta, Yashvit Sancheti, Greeva Kansara, Archan Trivedi, Rajiv PathakIndia2019-04-012019TV-14118 minHorror Movies, International MoviesWhen a doctor goes missing, his psychiatrist wife treats the bizarre medical condition of a psychic patient, who knows much more than he's leading on.201940[(when, a), (a, doctor), (doctor, goes), (goes, missing,), (missing,, his), (his, psychiatrist), (psychiatrist, wife), (wife, treats), (treats, the), (the, bizarre), (bizarre, medical), (medical, condition), (condition, of), (of, a), (a, psychic), (psychic, patient,), (patient,, who), (who, knows), (knows, much), (much, more), (more, than), (than, he's), (he's, leading), (leading, on.)]
9s10Movie1920Vikram BhattRajneesh Duggal, Adah Sharma, Indraneil Sengupta, Anjori Alagh, Rajendranath Zutshi, Vipin Sharma, Amin Hajee, Shri Vallabh VyasIndia2017-12-152008TV-MA143 minHorror Movies, International Movies, ThrillersAn architect and his wife move into a castle that is slated to become a luxury hotel. But something inside is determined to stop the renovation.2017129[(an, architect), (architect, and), (and, his), (his, wife), (wife, move), (move, into), (into, a), (a, castle), (castle, that), (that, is), (is, slated), (slated, to), (to, become), (become, a), (a, luxury), (luxury, hotel.), (hotel., but), (but, something), (something, inside), (inside, is), (is, determined), (determined, to), (to, stop), (stop, the), (the, renovation.)]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionyear_addedmonth_addedage_on_netflixbigrams
7777s7778TV ShowZombie DumbUnknownUnknownUnited States2019-07-012018TV-Y72 SeasonsKids' TV, Korean TV Shows, TV ComediesWhile living alone in a spooky town, a young girl befriends a motley crew of zombie children with diverse personalities.201971[(while, living), (living, alone), (alone, in), (in, a), (a, spooky), (spooky, town,), (town,, a), (a, young), (young, girl), (girl, befriends), (befriends, a), (a, motley), (motley, crew), (crew, of), (of, zombie), (zombie, children), (children, with), (with, diverse), (diverse, personalities.)]
7778s7779MovieZombielandRuben FleischerJesse Eisenberg, Woody Harrelson, Emma Stone, Abigail Breslin, Amber Heard, Bill Murray, Derek GrafUnited States2019-11-012009R88 minComedies, Horror MoviesLooking to survive in a world taken over by zombies, a dorky college student teams with an urban roughneck and a pair of grifter sisters.20191110[(looking, to), (to, survive), (survive, in), (in, a), (a, world), (world, taken), (taken, over), (over, by), (by, zombies,), (zombies,, a), (a, dorky), (dorky, college), (college, student), (student, teams), (teams, with), (with, an), (an, urban), (urban, roughneck), (roughneck, and), (and, a), (a, pair), (pair, of), (of, grifter), (grifter, sisters.)]
7779s7780TV ShowZona RosaUnknownManu NNa, Ana Julia Yeyé, Ray Contreras, Pablo MoránMexico2019-11-262019TV-MA1 SeasonInternational TV Shows, Spanish-Language TV Shows, Stand-Up Comedy & Talk ShowsAn assortment of talent takes the stage for a night of honest stand-up featuring four of Mexico's funniest LGBTQ comedians.2019110[(an, assortment), (assortment, of), (of, talent), (talent, takes), (takes, the), (the, stage), (stage, for), (for, a), (a, night), (night, of), (of, honest), (honest, stand-up), (stand-up, featuring), (featuring, four), (four, of), (of, mexico's), (mexico's, funniest), (funniest, lgbtq), (lgbtq, comedians.)]
7780s7781MovieZooShlok SharmaShashank Arora, Shweta Tripathi, Rahul Kumar, Gopal K. Singh, Yogesh Kurme, Prince DanielIndia2018-07-012018TV-MA94 minDramas, Independent Movies, International MoviesA drug dealer starts having doubts about his trade as his brother, his client, and two rappers from the slums each battle their own secret addictions.201870[(a, drug), (drug, dealer), (dealer, starts), (starts, having), (having, doubts), (doubts, about), (about, his), (his, trade), (trade, as), (as, his), (his, brother,), (brother,, his), (his, client,), (client,, and), (and, two), (two, rappers), (rappers, from), (from, the), (the, slums), (slums, each), (each, battle), (battle, their), (their, own), (own, secret), (secret, addictions.)]
7781s7782MovieZoomPeter HewittTim Allen, Courteney Cox, Chevy Chase, Kate Mara, Ryan Newman, Michael Cassidy, Spencer Breslin, Rip Torn, Kevin ZegersUnited States2020-01-112006PG88 minChildren & Family Movies, ComediesDragged from civilian life, a former superhero must train a new crop of youthful saviors when the military preps for an attack by a familiar villain.2020114[(dragged, from), (from, civilian), (civilian, life,), (life,, a), (a, former), (former, superhero), (superhero, must), (must, train), (train, a), (a, new), (new, crop), (crop, of), (of, youthful), (youthful, saviors), (saviors, when), (when, the), (the, military), (military, preps), (preps, for), (for, an), (an, attack), (attack, by), (by, a), (a, familiar), (familiar, villain.)]
7782s7783MovieZozoJosef FaresImad Creidi, Antoinette Turk, Elias Gergi, Carmen Lebbos, Viktor Axelsson, Charbel Iskandar, Yasmine AwadSweden, Czech Republic, United Kingdom, Denmark, Netherlands2020-10-192005TV-MA99 minDramas, International MoviesWhen Lebanon's Civil War deprives Zozo of his family, he's left with grief and little means as he escapes to Sweden in search of his grandparents.20201015[(when, lebanon's), (lebanon's, civil), (civil, war), (war, deprives), (deprives, zozo), (zozo, of), (of, his), (his, family,), (family,, he's), (he's, left), (left, with), (with, grief), (grief, and), (and, little), (little, means), (means, as), (as, he), (he, escapes), (escapes, to), (to, sweden), (sweden, in), (in, search), (search, of), (of, his), (his, grandparents.)]
7783s7784MovieZubaanMozez SinghVicky Kaushal, Sarah-Jane Dias, Raaghav Chanana, Manish Chaudhary, Meghna Malik, Malkeet Rauni, Anita Shabdish, Chittaranjan TripathyIndia2019-03-022015TV-14111 minDramas, International Movies, Music & MusicalsA scrappy but poor boy worms his way into a tycoon's dysfunctional family, while facing his fear of music and the truth about his past.201934[(a, scrappy), (scrappy, but), (but, poor), (poor, boy), (boy, worms), (worms, his), (his, way), (way, into), (into, a), (a, tycoon's), (tycoon's, dysfunctional), (dysfunctional, family,), (family,, while), (while, facing), (facing, his), (his, fear), (fear, of), (of, music), (music, and), (and, the), (the, truth), (truth, about), (about, his), (his, past.)]
7784s7785MovieZulu Man in JapanUnknownNasty CUnited States2020-09-252019TV-MA44 minDocumentaries, International Movies, Music & MusicalsIn this documentary, South African rapper Nasty C hits the stage and streets of Tokyo, introducing himself to the city's sights, sounds and culture.202091[(in, this), (this, documentary,), (documentary,, south), (south, african), (african, rapper), (rapper, nasty), (nasty, c), (c, hits), (hits, the), (the, stage), (stage, and), (and, streets), (streets, of), (of, tokyo,), (tokyo,, introducing), (introducing, himself), (himself, to), (to, the), (the, city's), (city's, sights,), (sights,, sounds), (sounds, and), (and, culture.)]
7785s7786TV ShowZumbo's Just DessertsUnknownAdriano Zumbo, Rachel KhooAustralia2020-10-312019TV-PG1 SeasonInternational TV Shows, Reality TVDessert wizard Adriano Zumbo looks for the next “Willy Wonka” in this tense competition that finds skilled amateurs competing for a $100,000 prize.2020101[(dessert, wizard), (wizard, adriano), (adriano, zumbo), (zumbo, looks), (looks, for), (for, the), (the, next), (next, “willy), (“willy, wonka”), (wonka”, in), (in, this), (this, tense), (tense, competition), (competition, that), (that, finds), (finds, skilled), (skilled, amateurs), (amateurs, competing), (competing, for), (for, a), (a, $100,000), ($100,000, prize.)]
7786s7787MovieZZ TOP: THAT LITTLE OL' BAND FROM TEXASSam DunnUnknownUnited Kingdom, Canada, United States2020-03-012019TV-MA90 minDocumentaries, Music & MusicalsThis documentary delves into the mystique behind the blues-rock trio and explores how the enigmatic band created their iconic look and sound.202031[(this, documentary), (documentary, delves), (delves, into), (into, the), (the, mystique), (mystique, behind), (behind, the), (the, blues-rock), (blues-rock, trio), (trio, and), (and, explores), (explores, how), (how, the), (the, enigmatic), (enigmatic, band), (band, created), (created, their), (their, iconic), (iconic, look), (look, and), (and, sound.)]